Dataset Information

Screening and identification of biomarkers for systemic sclerosis via microarray technology.

ABSTRACT: Systemic sclerosis (SSc) is a complex autoimmune disease. The pathogenesis of SSc is currently unclear, although like other rheumatic diseases its pathogenesis is complicated. However, the ongoing development of bioinformatics technology has enabled new approaches to research this disease using microarray technology to screen and identify differentially expressed genes (DEGs) in the skin of patients with SSc compared with individuals with healthy skin. Publicly available data were downloaded from the Gene Expression Omnibus (GEO) database and intra?group data repeatability tests were conducted using Pearson's correlation test and principal component analysis. DEGs were identified using an online tool, GEO2R. Functional annotation of DEGs was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Finally, the construction and analysis of the protein?protein interaction (PPI) network and identification and analysis of hub genes was carried out. A total of 106 DEGs were detected by the screening of SSc and healthy skin samples. A total of 10 genes [interleukin?6, bone morphogenetic protein 4, calumenin (CALU), clusterin, cysteine rich angiogenic inducer 61, serine protease 23, secretogranin II, suppressor of cytokine signaling 3, Toll?like receptor 4 (TLR4), tenascin C] were identified as hub genes with degrees ?10, and which could sensitively and specifically predict SSc based on receiver operator characteristic curve analysis. GO and KEGG analysis showed that variations in hub genes were mainly enriched in positive regulation of nitric oxide biosynthetic processes, negative regulation of apoptotic processes, extracellular regions, extracellular spaces, cytokine activity, chemo?attractant activity, and the phosphoinositide 3 kinase?protein kinase B signaling pathway. In summary, bioinformatics techniques proved useful for the screening and identification of biomarkers of disease. A total of 106 DEGs and 10 hub genes were linked to SSc, in particular the TLR4 and CALU genes.

SUBMITTER: Xu C

PROVIDER: S-EPMC6777682 | biostudies-literature | 2019 Nov

REPOSITORIES: biostudies-literature

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Screening and identification of biomarkers for systemic sclerosis via microarray technology.

Xu Chen C Meng Ling-Bing LB Duan Yu-Chen YC Cheng Yong-Jing YJ Zhang Chun-Mei CM Zhou Xing X Huang Ci-Bo CB

International journal of molecular medicine 20190905 5

Systemic sclerosis (SSc) is a complex autoimmune disease. The pathogenesis of SSc is currently unclear, although like other rheumatic diseases its pathogenesis is complicated. However, the ongoing development of bioinformatics technology has enabled new approaches to research this disease using microarray technology to screen and identify differentially expressed genes (DEGs) in the skin of patients with SSc compared with individuals with healthy skin. Publicly available data were downloaded fro ...[more]

PMID: 31545397

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Project description:BackgroundPatients with systemic sclerosis (SSc) are known to have higher incidence of atherosclerosis (AS). Mitochondrial injuries in SSc can cause endothelial dysfunction, leading to AS; thus, mitochondria appear to be hubs linking SSc to AS. This study aimed to identify the mitochondria-related biomarkers of SSc and AS.MethodsWe identified common differentially expressed genes (DEGs) in the SSc (GSE58095) and AS (GSE100927) datasets of the Gene Expression Omnibus (GEO) database. Considering the intersection between genes with identical expression trends and mitochondrial genes, we used the least absolute shrinkage and selection operator (LASSO) as well as random forest (RF) algorithms to identify four mitochondria-related hub genes. Diagnostic nomograms were then constructed to predict the likelihood of SSc and AS. Next, we used the CIBERSORT algorithm to evaluate immune infiltration in both disorders, predicted the transcription factors for the hub genes, and validated these genes for the two datasets.ResultsA total of 112 genes and 13 mitochondria-related genes were identified; these genes were then significantly enriched for macrophage differentiation, collagen-containing extracellular matrix, collagen binding, antigen processing and presentation, leukocyte transendothelial migration, and apoptosis. Four mitochondria-related hub DEGs (IFI6, FSCN1, GAL, and SGCA) were also identified. The nomograms showed good diagnostic values for GSE58095 (area under the curve (AUC) = 0.903) and GSE100927 (AUC = 0.904). Further, memory B cells, γδT cells, M0 macrophages, and activated mast cells were significantly higher in AS, while the resting memory CD4+ T cells were lower and M1 macrophages were higher in SSc; all of these were closely linked to multiple immune cells. Gene set enrichment analysis (GSEA) showed that IFI6 and FSCN1 were involved in immune-related pathways in both AS and SSc; GAL and SGCA are related to mitochondrial metabolism pathways in both SSc and AS. Twenty transcription factors (TFs) were predicted, where two TFs, namely BRCA1 and PPARγ, were highly expressed in both SSc and AS.ConclusionFour mitochondria-related biomarkers were identified in both SSc and AS, which have high diagnostic value and are associated with immune cell infiltration in both disorders. Hence, this study provides new insights into the pathological mechanisms underlying SSc and AS. The specific roles and action mechanisms of these genes require further clinical validation in SSc patients with AS.

Project description:Pulmonary arterial hypertension (PAH) is a rare but fatal disease characterized by vascular cell proliferation; the pathogenesis of PAH has yet to be fully elucidated. Publicly available genetic data were downloaded from the Gene Expression Omnibus (GEO) database, and gene set enrichment analysis (GSEA) was used to determine significant differences in gene expression between tissues with PAH and healthy lung tissues. Differentially expressed genes (DEGs) were identified using the online tool, GEO2R, and functional annotation of DEGs was performed using Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Next, the construction and module analysis of the protein-protein interaction (PPI) network and verification of the expression level of hub genes was performed. Finally, prediction and enrichment analysis of microRNAs associated with the hub genes was carried out. A total of 110 DEGs were detected by screening PAH and healthy lung samples. The expression of nine genes [polo-like kinase 4 (PLK4), centromere protein U, kinesin family member 20B, structural maintenance of chromosome 2 (SMC2), abnormal spindle microtubule assembly, Fanconi Anemia complementation group I, kinesin family member 18A, spindle apparatus coiled-coil protein 1, and MIS18 binding protein 1] was elevated in PAH; this was statistically significant compared with their expression in healthy lung tissue, and they were identified as hub genes. GO and KEGG analysis showed that the variations in DEGs were abundant in DNA-templated transcription, sister chromatid cohesion, mitotic nuclear division, cell proliferation, and regulation of the actin cytoskeleton. In conclusion, this study has successfully identified hub genes and key pathways of PAH, with a total of 110 DEGs and nine hub genes related to PAH, especially the PLK4 and SMC2 genes, thus providing important clues for the in-depth understanding of the molecular mechanism of PAH and providing potential therapeutic targets.

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Screening and identification of biomarkers for systemic sclerosis via microarray technology.

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Screening and identification of biomarkers for systemic sclerosis via microarray technology.

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